Description
Nutrient availability and physical constraints strongly regulate bacterial growth in microscale environments. In spatially structured systems, confinement, fluid flow, and consumption by the bacterial population generate nutrient microgradients that lead to heterogeneous growth and spatially organised metabolic states. Bacteria adapt to multiple carbon sources either through co-utilisation, where nutrients are consumed simultaneously, or through hierarchical uptake, in which bacterial cells preferentially metabolise the most favourable carbon source and only later switch to alternative substrates. This sequential strategy gives rise to lag phases—transient non-growing adaptation periods whose duration depends on nutrient composition and strain. While lag phases are well characterised in bulk cultures, their spatial organisation and coupling to microscale nutrient transport remain poorly understood.
Here, we use microfluidic family machine devices combined with time-lapse fluorescence microscopy to investigate how nutrient microgradients, lag phases, and spatial confinement jointly shape bacterial growth in non–well-mixed environments. These microfluidic chips enable the controlled growth of E. coli monolayers under precisely defined geometrical constraints and steady nutrient flows, allowing direct manipulation of the bacterial physicochemical microenvironment.
We developed a physical model describing carbon diffusion and hierarchical uptake in spatially confined populations under controlled nutrient influx. The model predicts a trade-off between growth on less favourable carbon sources—promoted by microgradients—and the duration of lag phases, which together determine population-level growth dynamics.
Microfluidic experiments with fluorescent E. coli strains quantitatively validate the model predictions, revealing how nutrient microgradients imposed by flow and confinement spatially regulate metabolic adaptation at single-cell resolution. By combining microfluidic manipulation, fluorescence imaging, single-cell image analysis, and modelling, this work establishes a quantitative framework to study microbial growth and adaptation in structured environments, highlighting the power of microfluidics to dissect microbial physiology at the microscale.